Using light to predict fuels-reduction and group-selection effects on succession in Sierran mixed-conifer forest
Bibliographic record
Abstract
Many semi-arid coniferous forests in western North America have reached historically unprecedented densities over the past 150 years and are dominated by shade-tolerant trees. Silvicultural treatments generally open the canopy but may not restore shade-intolerant species. We determined crossover-point irradiance (CPI) (light at which the height growth rank of pairs of species changes) for seedlings in Sierra Nevada mixed-conifer forest and used these to interpret light environments produced by fuels-reduction thinning and group selection with reserved large trees. Nine of 21 species pairs had well-defined CPIs. The CPI of the most common shade-tolerant and intolerant species (white fir ( Abies concolor (Gordon & Glendl.) Lindl. ex Hildebr.) and ponderosa pine ( Pinus ponderosa Douglas ex P. Lawson & C. Lawson)) was 22.5 mol·m–2·day–1or 41% of full sun. Median understory irradiance increased from 9.2 mol·m–2·day–1(17% full sun) in pretreatment forest to 13 mol·m–2·day–1(24% full sun) in lightly and 15.5 mol·m–2·day–1(28% full sun) in moderately thinned stands and 37 mol·m–2·day–1(67% full sun) in group-selection openings. We estimate that 5%–20% of ground area in lightly to moderately thinned stands would have enough light to favor shade-intolerant over shade-tolerant growth compared with 89% of ground area in group-selection openings. The CPI provides a tool to assess regeneration implications of treatment modification such as increasing heterogeneity of thinning to enhance regeneration or reserving large trees in group-selection openings to maintain wildlife habitat.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".